



'''simple docstring'''

import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def 				a  (     UpperCamelCase_    :    Optional[int]      ,					UpperCamelCase_    :    Union[str, Any]      ,					UpperCamelCase_    :    int      ,					UpperCamelCase_    :    Optional[int]			)       ->	List[Any]:
		if isinstance(UpperCamelCase_      ,					UpperCamelCase_			):
				snake_case__	     =np.full((len(UpperCamelCase_			), sequence_length, 2)      ,					UpperCamelCase_			)
		else:
				snake_case__	     =np.full((len(UpperCamelCase_			), sequence_length)      ,					UpperCamelCase_			)

		for i, tensor in enumerate(UpperCamelCase_			):
				if padding_side == "right":
						if isinstance(UpperCamelCase_      ,					UpperCamelCase_			):
								snake_case__	     =tensor[:sequence_length]
						else:
								snake_case__	     =tensor[:sequence_length]
				else:
						if isinstance(UpperCamelCase_      ,					UpperCamelCase_			):
								snake_case__	     =tensor[:sequence_length]
						else:
								snake_case__	     =tensor[:sequence_length]

		return out_tensor.tolist()






def 				a  (     UpperCamelCase_    :    str			)       ->	Optional[Any]:
		snake_case__	     =ord(UpperCamelCase_			)
		if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
				return True
		snake_case__	     =unicodedata.category(UpperCamelCase_			)
		if cat.startswith('P'			):
				return True
		return False


@dataclass
class 							a__( snake_case__     ):
			a_	: PreTrainedTokenizerBase
			a_	: Union[bool, str, PaddingStrategy]									=      True
			a_	: Optional[int]									=      None
			a_	: Optional[int]									=      None
			a_	: int									=      -1_0_0
			a_	: str									=      "pt"






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Optional[Any]:
					import torch

					snake_case__	     ='label' if 'label' in features[0].keys() else 'labels'
					snake_case__	     =[feature[label_name] for feature in features] if label_name in features[0].keys() else None
					snake_case__	     =self.tokenizer.pad(
					    _UpperCAmelCase					,   padding=self.padding					,   max_length=self.max_length					,   pad_to_multiple_of=self.pad_to_multiple_of					,   return_tensors='pt' if labels is None else None					,   )

					if labels is None:
							return batch

					snake_case__	     =torch.tensor(batch['entity_ids']						).shape[1]
					snake_case__	     =self.tokenizer.padding_side
					if padding_side == "right":
							snake_case__	     =[
							    list(_UpperCAmelCase						) + [self.label_pad_token_id] * (sequence_length - len(_UpperCAmelCase						)) for label in labels
							]
					else:
							snake_case__	     =[
							    [self.label_pad_token_id] * (sequence_length - len(_UpperCAmelCase						)) + list(_UpperCAmelCase						) for label in labels
							]

					snake_case__	     =[feature['ner_tags'] for feature in features]
					snake_case__	     =padding_tensor(_UpperCAmelCase					,   -1					,   _UpperCAmelCase					,   _UpperCAmelCase						)
					snake_case__	     =[feature['original_entity_spans'] for feature in features]
					snake_case__	     =padding_tensor(_UpperCAmelCase					,   (-1, -1)					,   _UpperCAmelCase					,   _UpperCAmelCase						)
					snake_case__	     ={k: torch.tensor(_UpperCAmelCase					,   dtype=torch.intaa						) for k, v in batch.items()}

					return batch









'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_camembert import CamembertTokenizer
else:
			SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  None


SCREAMING_SNAKE_CASE__     :						Tuple	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						List[Any]	=  {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE__     :						List[Any]	=  {
    '''camembert-base''': 5_1_2,
}

SCREAMING_SNAKE_CASE__     :						Any	=  '''▁'''

class 							a__( snake_case__     ):
			a_	: Tuple									=      VOCAB_FILES_NAMES
			a_	: Optional[int]									=      PRETRAINED_VOCAB_FILES_MAP
			a_	: List[str]									=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a_	: str									=      ['''input_ids''', '''attention_mask''']
			a_	: Union[str, Any]									=      CamembertTokenizer






			def __init__(     self					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="<unk>"					,   _UpperCAmelCase="<pad>"					,   _UpperCAmelCase="<mask>"					,   _UpperCAmelCase=["<s>NOTUSED", "</s>NOTUSED"]					,   **_UpperCAmelCase					,   )							->							str:
					# Mask token behave like a normal word, i.e. include the space before it
					snake_case__	     =AddedToken(_UpperCAmelCase					,   lstrip=_UpperCAmelCase					,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						) else mask_token

					super().__init__(
					    _UpperCAmelCase					,   tokenizer_file=_UpperCAmelCase					,   bos_token=_UpperCAmelCase					,   eos_token=_UpperCAmelCase					,   sep_token=_UpperCAmelCase					,   cls_token=_UpperCAmelCase					,   unk_token=_UpperCAmelCase					,   pad_token=_UpperCAmelCase					,   mask_token=_UpperCAmelCase					,   additional_special_tokens=_UpperCAmelCase					,   **_UpperCAmelCase					,   )

					snake_case__	     =vocab_file
					snake_case__	     =False if not self.vocab_file else True






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							List[int]:

					if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					snake_case__	     =[self.cls_token_id]
					snake_case__	     =[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							List[int]:
					snake_case__	     =[self.sep_token_id]
					snake_case__	     =[self.cls_token_id]

					if token_ids_a is None:
							return len(cls + token_ids_a + sep						) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							Tuple[str]:
					if not self.can_save_slow_tokenizer:
							raise ValueError(
							    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
							    'tokenizer.'						)

					if not os.path.isdir(_UpperCAmelCase						):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
							return
					snake_case__	     =os.path.join(
					    _UpperCAmelCase					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

					if os.path.abspath(self.vocab_file						) != os.path.abspath(_UpperCAmelCase						):
							copyfile(self.vocab_file					,   _UpperCAmelCase						)

					return (out_vocab_file,)




